I'm dealing with reservoir boundaries segmentation. My study area is Vietnam and I try on Landsat 7 ETM+/8 dataset.
But at some point of time, cloud cover has covered almost reservoir water body. For example, with Tri An Reservoir (Landsat 8 dataset can be found at here), the water body has been covered by cloud (approx. 51.6%).
So there are anyways to recover the water pixels have been replaced by cloud pixels? For more explaination, could I get the water body under cloud cover as high accurate as possible?
You're out of luck. Cloud detection is hard problem. Cloud removal would require, well, reconstructing data you do not have.
The way this is most commonly dealt with is to use repeat photography to try to find a day when you can see what you're looking for. If this isn't possible, you're out of luck.
Related
We're using ogr2ogr and tippecanoe to generate MBTILES files and then uploading them to Mapbox. This incurs Mapbox's recent-ish tile processing and hosting costs.
We've been hit by a couple of surprise bills. We can work around this by following the advice on https://docs.mapbox.com/help/troubleshooting/manage-tileset-costs/#how-do-i-lower-my-tileset-processing-costs.
But there doesn't seem to be an obvious way to estimate tile processing costs before the upload occurs. I'd like to display the tileset's kilometers squared figure, resolution, and calculate a cost to show to our team before the upload occurs.
Does anyone know how this could be achieved?
Turns out Mapbox does have a tool for this, but it's not linked from any of their documentation.
See https://github.com/mapbox/tilesets-cli/#estimate-area
Example usage:
# tilesets estimate-area tennis.json -p 30cm
{"km2": "502912", "precision": "30cm", "pricing_docs": "For more information, visit https://www.mapbox.com/pricing/#tilesets"}
I am capturing static images of particulate biological materials on the millimeter scale, and then processing them in MATLAB. My routine is working well so far, but I am using a rudimentary calibration procedure where I include some coins in the image, automatically find them based on their size and circularity, count their pixels, and then remove them. This allows me to generate a calibration line with input "area-mm^2" and output "Area- pixels," which I then use to convert the pixel area of the particles into physical units of millimeters squared.
My question is: is there a better calibrant object that I can use, such as a stage graticule or "phantom" as some people seem to call them? Do you know where I could purchase such a thing? I can't even seem to find a possible vendor. Is there another rigorous way to approach this problem without using calibrant objects in the field of view?
Thanks in advance.
Clay
Image calibration is always done using features of knowns size or distance.
You could calculate the scale based on nominal specifications but your imaging equipment will always have some production tolerances, your object distance is only known to a certain accuracy...
So it's always safer and simpler to actually calibrate your scale.
As a calibrant you can use anything that meets your requirements. If you know the size well enough and if you are able to extract it's dimensions in pixels properly you can use it...
I don't know your requirements and your budget, but if you want something very precise and fancy you can use glass masks.
There are temperature stable glass slides that are coated with chrome for example. There are many companies that produce such masks customized (IMT AG, BVM maskshop, ...) Also most optics lab equipment suppliers have such things on stock. Edmund Optics, Newport, ...
I was wondering if someone can provide me a guideline to detect if a person in a picture is bald or not, or even better, how much hair s\he has.
So far I tried to detect the face and the eyes position. From that information, I roughly estimate the forehead and bald area by cutting the area above the eyes as high as some portion of the face.
Then I extract HOG features and train the system with bald and not-bald images using SVM.
Now when I'm looking at the test results, I see some pictures classified as bald but some of them actually have blonde hair or long forehead that hair is not visible after the cutting process. I'm using MATLAB for these operations.
So I know the method seems to be a bit naive, but can you suggest a way of finding out the bald area or extracting the hair, if exists. What method would be the most appropriate for that kind of problem?
very general, so answer is general unless further info provided
Use Computer Vision (e.g MATLAB Computer Vision toolkit) to detect face/head
head has analogies (for human faces), using these one can get the area of the head where hair or baldness is (it seems you already have these)
Calculate the (probabilistic color space model) range where the skin of the person lies (most peorple have similar skin collor space range)
Calculate percentage of skin versus other color (meaning hair) in that area
You have it!
To estimate a skin color model check following papers:
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.56.8637&rep=rep1&type=pdf
http://infoscience.epfl.ch/record/135966
http://www.eurasip.org/Proceedings/Eusipco/Eusipco2010/Contents/papers/1569293757.pdf
Link
If an area does not fit well with skin model it can be taken as non-skin (meaning hair, assuming no hats etc are present in samples)
Head region is very small, hence, using HOG for classification doesn't make much sense.
You can use prior information - like detect faces; baldness/hair is certain to be found on the area above the face. Also, use some denser feature descriptors.
You are probably ending up with very sparse representation or equivalently less information because of which your classifier is not able to classify correctly.
I have a image with noise. i want to remove all background variation from an image and want a plain image .My image is a retinal image and i want only the blood vessel and the retinal ring to remain how do i do it? 1 image is my original image and 2 image is how i want it to be.
this is my convoluted image with noise
There are multiple approaches for blood vessel extraction in retina images.
You can find a thorough overview of different approaches in Review of Blood Vessel Extraction Techniques and Algorithms. It covers prominent works of many approache.
As Martin mentioned, we have the Hessian-based Multiscale Vessel Enhancement Filtering by Frangi et al. which has been shown to work well for many vessel-like structures both in 2D and 3D. There is a Matlab implementation, FrangiFilter2D, that works on 2D vessel images. The overview fails to mention Frangi but cover other works that use Hessian-based methods. I would still recommend trying Frangi's vesselness approach since it is both powerful and simple.
Aside from the Hesisan-based methods, I would recommend looking into morphology-based methods since Matlab provides a good base for morphological operations. One such method is presented in An Automatic Hybrid Method for Retinal Blood Vessel Extraction. It uses a morphological approach with openings/closings together with the top-hat transform. It then complements the morphological approach with fuzzy clustering and some post processing. I haven't tried to reproduce their method, but the results look solid and the paper is freely available online.
This is not an easy task.
Detecting boundary of blood vessals - try edge( I, 'canny' ) and play with the threshold parameters to see what you can get.
A more advanced option is to use this method for detecting faint curves in noisy images.
Once you have reasonably good edges of the blood vessals you can do the segmentation using watershed / NCuts or boundary-sensitive version of meanshift.
Some pointers:
- the blood vessals seems to have relatively the same thickness, much like text strokes. Would you consider using Stroke Width Transform (SWT) to identify them? A mex implementation of SWT can be found here.
- If you have reasonably good boundaries, you can consider this approach for segmentation.
Good luck.
I think you'll be more served using a filter based on tubes. There is a filter available which is based on the work done by a man called Frangi, and the filter is often dubbed the Frangi filter. This can help you with identifying the vasculature in the retina. The filter is already written for Matlab and a public version is available here. If you would like to read about the underlying research search for: 'Multiscale vessel enhancement', by Frangi (1998). Another group who's done work in the same field are Sato et.al.
Sorry for the lack of a link in the last one, I could only find payed sites for looking at the research paper on this computer.
Hope this helps
Here is what I will do. Basically traditional image arithmetic to extract background and them subtract it from input image. This will give you the desired result without background. Below are the steps:
Use a median filter with large kernel as the first step. This will estimate the background.
Divide the input image with the output of step 1 [You may have to shift the denominator a little (+1) ] to avoid divide by 0.
Do the quantization to 8 or n bit integer based on what bit the original image is.
The output of step 3 above is the background. Subtract it from original image, to get the desired result. This clips all the negative values as well.
I have a program that uses the API to get altitude data of the screen by running through hundreds of thousands of latitude and longitude points. However, Google Earth's data refines itself as one zooms in. So I need to know the minimum altitude I can use to get the best data Google has to offer. I am at a loss to figure out how to do this.
Furthermore, I assume that this depends on the part of the world. I'm going to assume the best part of the world, always.
Thanks for any feedback.
There is no API for determining the quality of the underlying imagery. While this question is not an exact duplicate of Get ground altitude in a reliable way, the same general algorithm can be applied:
Zoom to the required lat/lon at altitude Z
Wait for the imagery to come in (as described in the linked question)
Zoom in "closer" (Z - [delta])
If Google has better imagery, the loaded percentage should drop, wait for it load
Repeat steps 3-4 until the progress is always ~100% (e.g., until no loading is required)
Even with this hack, I'm not sure it will yield reliable results. Your question is flawed for several reasons:
What does "the best data Google has to offer" mean?
You have a lat/lon coordinate. That's a single, infinitely small, point on the Earth
Raw satellite imagery typically covers large sections (many lat/lon points), so the "best" height would be to set the GE camera so that your view convers exactly one satellite image tile
Why do you need the "best" data?
Satellite imagery is expressed in terms of "area covered by a single pixel" (e.g., 1m = 1 pixel in the image covers 1sq m)
Knowing this, the camera height will vary image by image, even for tiles within the same satellite imagery data set (it shouldn't vary much -- but it could slightly.)
Are you trying to cache the imagery off the globe? If so, you should carefully review the TOS to make sure you're not in violation.
Note that if you are just looking to obtain satellite imagery -- there are many good (free) sources. USGS National Map Viewer should be able to get you pretty good imagery for the US (including territories).